Computers, Environment and Urban Systems 32 (2008) 377–385
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Computers, Environment and Urban Systems journal homepage: www.elsevier.com/locate/compenvurbsys
Design and implementation of an on-demand feature extraction web service to facilitate development of spatial data infrastructures A. Mansourian *, M.J. Valadan Zoje, A. Mohammadzadeh, M. Farnaghi Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, No. 1346, Vali-e-asr St., Mirdamad, P.O. Box 15875-4416, 19967-15433 Tehran, Iran
a r t i c l e
i n f o
Article history: Received 24 February 2008 Received in revised form 10 July 2008 Accepted 11 July 2008
Keywords: Spatial data infrastructure (SDI) Web services On-demand data production Automatic feature extraction Remote sensing
a b s t r a c t One of the main problems in urban and environmental management concerns the unavailability of reliable spatial data in a spatial data infrastructure (SDI) environment. The main reason for the problem of spatial data availability is the time-consuming nature of their manual production. The present paper proposes the development of on-demand data production Web services for the Internet using feature extraction techniques from satellite images as a solution to the problem. Such services allow users to connect to an on-demand data production Web service to produce the required data automatically if the users cannot find the required spatial data. In order to address and investigate this suggestion, a prototype system is developed. We have developed and implemented a system for automatic road extraction and describe it in special detail with a case study. Web service technologies and OGC (open geospatial consortium) frameworks are utilized for the development of the system to satisfy data and access interoperability in a SDI environment. The paper explains that the on-demand feature extraction Web service can facilitate the development of SDI by resolving the problem of spatial data availability. It also describes further research and different topics that should be considered in the development of SDIs to make such Web services operational. Ó 2008 Elsevier Ltd. All rights reserved.
1. Introduction The development of spatial data infrastructure (SDI) has evolved as a central driving force in the management of spatial information over the last decade (Williamson, Grant, & Rajabifard, 2005). More than half the world’s countries claim that they are involved in some form of SDI development (Crompvoets, Rajabifard, Bregt, & Williamson, 2004). SDI concepts and models have also been recently used in different applications, such as disaster management (Mansourian, Rajabifard, Valadan Zoje, & Williamson, 2006), natural resource management, and wireless applications (Davies, 2003). With such wide range of activities, Masser (2005a) used the term ‘SDI phenomenon’ to describe the events that have taken place in this field over the last 10–15 years. SDI is an initiative that intends to create an environment that will enable a wide variety of users to access, retrieve, and disseminate spatial data in an easy and secure way. In principle, SDIs allow the sharing of data; this is extremely useful, because it enables users like urban and environment managers to save resources, time, and energy when trying to acquire new datasets
* Corresponding author. Tel.: +98 2188786212; fax: +98 2188786213. E-mail addresses:
[email protected],
[email protected] (A. Mansourian),
[email protected] (M.J. Valadan Zoje), ali_mohammadzadeh2002@ yahoo.com (A. Mohammadzadeh),
[email protected] (M. Farnaghi). 0198-9715/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.compenvurbsys.2008.07.002
by avoiding duplication of expenses associated with the generation, maintenance, and integration of data. SDI aims to establish the relationship between people and data through appropriate policy-making, standardization activities, and the creation of accessing networks (the general SDI model: Rajabifard, Feeney, & Williamson, 2002). SDI is also an integrated, multi-level hierarchy of interconnected SDIs based on collaboration and partnerships among different stakeholders (SDI hierarchy: Rajabifard, Feeney, & Williamson, 2003). Development of SDI is a matter of different technical, technological, social, institutional, and economical challenges (de Man, 2006; Mansourian et al., 2006; Masser, 2005b; Masser, Rajabifard, & Williamson, 2007; Williamson, Rajabifard, & Feeney, 2003). One of the important problems relates to the gap in spatial data (INSPIRE, 2003; Karatunga, 2002; Omran, Crompvoets, & Bregt, 2006). The ‘gap’ in spatial data relates to unavailability of required data and/or the unreliability of available data due to low accuracy, being out-of-date, incompleteness, and similar issues. This problem is more critical in urban environments because of their fast development and changes. Since current manual methods for the production of spatial data is time-consuming and expensive, this issue is considered as an important problem for the development of SDI at its initial stages. Remote sensing is a multidisciplinary science and technology that can collect data from any area in a short period of time. It
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offers a good solution that fulfills the data requirements of SDIs. Remote sensing offers data as an image in raster format, however, and most of the GIS (geographical information system) users would like to have spatial data at the feature level in a vector format. Automatic/semi-automatic feature extraction techniques from remote sensing data allow feature level data to be obtained with lower costs and shorter times than manual/traditional techniques. This paper suggests the establishment of on-line Web services that provide users with data using an on-demand data production approach based on automatic/semi-automatic feature extraction techniques from satellite images. Web services equipped with automated/semi-automated feature extraction engines can thus be established. If the datasets required by a user are not available, then the user can connect to the Web service through the Internet, introduce the extent of the region of interest, and enter the required data layers to the engine. The system chooses the appropriate available satellite images from archive. Datasets that the user needs are produced automatically using advanced information processing techniques. The produced data can be added to the list of a spatial clearinghouse or catalogue service in order to make the data searchable and accessible for other users in the future. Different challenges for making such a service operational and usable exist. From a SDI perspective, one technical challenge relates to spatial interoperability. Based on the OGC1 Reference Model,2 spatial interoperability refers to the ‘‘capability to communicate, execute programs, or transfer spatial data among various functional units in a manner that requires the user to have little or no knowledge of the unique characteristics of those units”. As this definition suggests the non-interoperability of spatial processing systems hampers the sharing of spatial data and services among software applications. In this context, two kinds of non-interoperability – data and access non-interoperability – can be identified3 (Peng, 2004). Data non-interoperability implies that different spatial processing systems use internal data formats and produce data in formats that are different and in most cases proprietary. As a result, data sharing among different systems in a SDI environment is difficult. Access non-interoperability means that different spatial processing systems use proprietary software access methods with proprietary software interfaces, which restrict inter-process communication among various spatial processing systems. In other words, interface definition languages, communication protocols, communication ports, and even object transfer mechanisms vary in each software development platform. The software platform, which is used to develop the spatial processing system, thus imposes the use of specific and proprietary communication methods among various parts of the system. For this reason, different spatial processing systems developed by different software development platforms cannot communicate and share services automatically in an interoperable manner. This paper suggests an integrated utilization of Web service technologies (from the IT world) as well as OGC’s specifications and encodings (from the spatial information community) to resolve the non-interoperability problem for the proposed Web service. In the context of this research, the development of a Web service that uses automatic feature extraction techniques to produce data for users in a SDI environment is ongoing. OGC frameworks and Web service technologies are used for the development of
1
Open Geospatial Consortium. The OpenGISÒ Reference Model. Available at: http://portal.opengeospatial.org/ files/?artifact_id=3836, visited on October 2005. 3 In addition to the technical problems associated with the current spatial processing system, semantic problems also exist that are out of the scope of this paper. 2
the system to provide spatial interoperability between the system and users. This paper describes the results of the project, including the development of a prototype system. It is notable that current studies indicate that the deployment of spatial Web services is known as a significant factor facilitating the development of SDIs (Najar, Rajabifard, Williamson, & Giger, 2007). Web services can support the user in processing, accessing, and visualizing data. Most current activities developing spatial Web services and relevant standards focus on access (e.g. catalogue services) and visualization (e.g. Web Map Services), whereas less attention has been paid to processing aspects in the SDI environment. This research also addresses the benefits that can be gained from spatial Web services in the context of processing tasks within a SDI environment.
2. Methodology and framework As highlighted earlier, automatic feature extraction techniques, OGC frameworks, and Web service technologies are three main components required in the development of a Web-based service that (1) has the capability of on-demand data production from remote sensing data and (2) acts as a step forward to facilitate SDI development. This section describes the algorithm, methodologies, and frameworks utilized for development of the prototype system. 2.1. Automatic Feature Extraction Algorithm Automatic feature extraction methods utilize geometric and spectral information to recognize different objects existing on the Earth’s surface. Traditionally, most of the objects are categorized in three main groups: point type, linear type, and planimetric type. Because of the availability of the very high resolution multispectral images, some well-known linear type objects like rivers or road networks also appear as planimetric objects in those types of images. Object surfaces in the high resolution images include more details and complexity. Therefore, more sophisticated and complex mathematical algorithms should be employed to extract them. Object extraction methodologies developed by other researchers utilize diverse solutions. No common approach for extracting all types of features exists, however, and the proposed algorithms are valid only in the definite conditions explained by the developer. Additionally, changes of image resolution or the type of the region being imaged have serious effects on the results for a specific object. Currently, most research dealing with automatic feature extraction from satellite imagers has focused on extracting buildings and roads as two major object types. In building extraction, Rottensteiner, Trinder, Clode, and Kubike (2007), Sohn and Dowman (2007) and Suveg and Vosselman (2004) have fused optical images with LIDAR or SAR data to extract buildings efficiently with a less complex reconstruction process. Jaynes, Stolle, and Collins (1994) have employed low level algorithms limited to corner extraction, edge line linking, and grouping in order to reconstruct the shapes of buildings. Other research focuses on extracting 3D building objects. To achieve precise and accurate 3D reconstructed building models, two stereo images should be utilized. Noronha and Nevatia (2001) have generated hypotheses from the analysis of single images and then used stereo images to validate them. Baillard and Zisserman (2000) have directly used stereo images to reconstruct the 3D surface of regions, and geometric models of the objects have been obtained using the generated 3D surface. For road extraction, Hu, Zhang, and Tao (2004) and Kim, Park, Kim, Jeong, and Kim (2004) have used image matching techniques to track road segments. Amini, Saradjian, Blais, Lucas, and Azizi (2002) simplify images using morphological operators and then
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apply a morphological skeletonization algorithm to extract the road skeleton. Agouris, Gyftakis, and Stefanidis (2001), Agouris, Stefanidis, and Gyftakis (2001) and Péteri, Couloigner, and Ranchin (2004) have used snakes and dynamic programming for the extraction of roads. Niu (2006) has implemented a semi-automatic system based on geometric deformable models to extract roads and vehicles. Mohammadzadeh, Tavakoli, and Valadan Zoej (2006) proposed a new fuzzy method to calculate road mean radiometric values in each image band. Doucette, Agouris, Stefanidis, and Musavi (2001) used a self-organizing road map (SORM) neural network for road centerline identification. Zhang and Couloigner (2006) used angular texture signatures for the discrimination of parking lots and roads in Quickbird images. Mokhtarzade and Valadan Zoej (2007) examined texture information to design an appropriate multilayer neural network to detect road surfaces. Chen, Wang, and Zhang (2004) and Hinz and Baumgartner (2003) have applied resolution-dependent analysis to identify roads. Lisini, Tison, Tupin, and Gamba (2006) proposed the fusion of statistical information (classification) and structural information (detected lines) to extract road networks from SAR images. As roads are both infrastructural data and important objects existing in most satellite image scenes, we chose to study road extraction in this paper for the case study. In this case study, we develop an approach for extracting road centerlines. There are three important steps in the extraction of road centerlines. First, fuzzy C-means clustering is applied to segment the input high resolution color satellite image. Fuzzy C-means (FCM) clustering was proposed by Dunn (1974) and extended by Bezdek (1981), and it is one of the most well-known methodologies in clustering analysis. The number of classes is given by the user to the algorithm. Using FCM clustering, input spectral vectors are partitioned into ‘‘C” clusters. The cluster center and fuzzy partitioning matrix U are defined by minimizing the cost function J defined below
g rbc f ¼ ðf g bcÞ1 ¼ ððððf _ gÞg bcÞg bcÞ . . . g bcÞ; |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
ð6Þ
1
where
n ¼ 1 : f g bc ¼ ððf bcÞ _ gÞ: Opening by reconstruction (f bc b) is defined as
f bc b ¼ ðf bÞ Dbc f ;
ð7Þ
where g Dbc f is the dilation of ‘‘g” by ‘‘bc” conditioned to ‘‘f” and is defined as
g Dbc f ¼ ðf g bcÞ1 ¼ ððððf ^ gÞg bcÞg bcÞ g bcÞ; |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
ð8Þ
1
where
n ¼ 1 : f g bc ¼ ððf bcÞ ^ gÞ: To the best of our knowledge, this is the first time that RASF has been used in automatic road extraction from satellite images. The results of FCM and RASF will be compared to evaluate its efficiency. In the third step, another morphology algorithm called ‘‘skeletonization” is used to extract road centerlines. Additionally, small redundant segments are removed to obtain the skeleton of the road. The extracted roads are finally converted into vectors using a raster-to-vector conversion algorithm. 2.2. OGC framework
In the second step, a morphological algorithm called ‘‘Reconstructive Alternating Sequential Filtering (RASF)” (Crespo, Schafer, Serra, Gratin, & Meyer, 1998) is implemented and used to detect road surfaces. Morphological operators of closing and opening by reconstruction are iteratively applied (n times) to filter the input image. From Eq. (3), the parameters defined in Eqs. (4)–(8) are as follows:
OGC is an international consensus standards organization that is leading the development of standards for geospatial and location-based services. OGC specifications support interoperable solutions that ‘‘geo-enable” the Web, wireless and location-based services, and mainstream IT. The specifications empower technology developers to make complex spatial data and services accessible and useful with all kinds of applications. Geographic Markup Language (GML) and Web Map Service (WMS), the well-known OGC’s encoding and specification, respectively, were utilized in this research work. According to GML encoding standards (OGC, 2007), ‘‘GML is an XML4 grammar written in XML Schema for the description of application schemas as well as the transport and storage of geographic information”. GML is known as a standard format for exchanging and sharing spatial data; currently, most GIS software can understand GML. In this research, extracted features are converted into GML to achieve data interoperability. A WMS dynamically produces maps of spatially referenced data from geographic information (OGC, 2006). This standard defines a ‘‘map” to be a portrayal of geographic information as a digital image file suitable for display on a computer screen. A map is not the data itself. WMS-produced maps are generally rendered in pictorial formats like PNG, GIF, or JPEG. In this research, a basic WMS was developed to satisfy parts of the service that required viewing or navigating data.
RASFnb;bc ¼ ðððððf bc bÞ bc bÞ bc 2bÞ bc 2bÞ bc nbÞ bc nb:
2.3. Web service technologies
JðU; v1 ; v2 ; . . . ; vc ; XÞ ¼
c X n X i¼1
2
um ij dij ;
ð1Þ
j¼1
where dij is the Euclidian distance between Xj and cluster center vi is defined as:
dij ¼
qX ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi s ðv xjk Þ2 : k¼1 jk
ð2Þ
It should be noted that c X
uij ¼ 1 8j ¼ 1; . . . ; n:
i¼1
ð3Þ
In the above formula, ‘‘nb” as the Minkowski addition of ‘‘b” (a structural element of size 3 * 3) is an iterative dilation ‘‘n 1” times and can be calculated as
nb ¼ ððb bÞ . . . bÞ |fflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
for n P 1:
ð4Þ
n1
Closing by reconstruction (f bc b) is defined as
f bc b ¼ ðf bÞ rbc f ;
ð5Þ
where g rbc f is the erosion of ‘‘g” by ‘‘bc” conditioned to ‘‘f” and can be calculated through
The Web is intended for human consumption. Data is consequently presented in a form that is human-readable, but this form of representation is error prone and difficult for applications to examine, extract, and use – both automatically and programmatically. Therefore, a need for application-to-application communication exists; this is the idea of the application-centric Web. The core idea of the application-centric Web is to provide software applications with the ability to communicate across various 4
eXtensible Markup Language.
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platforms without the intervention of human beings. In other words, automatic and direct communication among functional units running on heterogeneous platforms are the unique characteristics of the application-centric Web. Promising technologies for this kind of communication are Web service technologies (Newcomer, 2002). Web services provide a fundamentally new framework and consist of a set of standards for supporting network transportation, service communication, service publication, and service discovery. The technologies that form the foundations of Web services are Simple Object Access Protocol (SOAP), Web Services Description Language (WSDL), and Universal Description, Discovery, and Integration (UDDI) (Chatterjee and Webber, 2003). SOAP is an XML-based mechanism for exchanging information between applications within a distributed environment. This information exchange mechanism can be used to send messages between applications and implementing remote procedure calls (RPCs). RPCs allow one application to invoke and use a procedure (or capability) of another, possibly remote, application. SOAP does not specify any application implementation or programming model. Instead, it provides a mechanism for expressing application semantics that can be understood by applications regardless of their implementation. Accordingly, SOAP is application languageand platform-independent. SOAP is typically used in conjunction with HTTP, which supports easy traversal of firewalls and is sufficiently lightweight to be used within mobile and wireless environments. WSDL is an XML-based language for describing Web services. Through a WSDL description, a client application can determine the location of the remote Web service, the functions it implements, and how to access and use each function. After parsing a WSDL description, a client application can appropriately format a SOAP request and dispatch it to the location of the Web service. WSDL descriptions accompany the development of a new Web service and are created by the producer of the service. WSDL files (or pointers thereto) are typically stored in registries that can be searched by potential users to locate Web service implementations of desired capabilities. UDDI is a specification for a registry of information for Web services. UDDI defines a means to publish and discover (or search for) information about Web services like WSDL files. After browsing through an UDDI registry for information regarding the available Web services, the WSDL for the selected service can be parsed and an appropriate SOAP message sent to the service. Fig. 1 graphically illustrates the relationships between SOAP, WSDL, and UDDI. From a technical standpoint, each Web service has three main parts: service description, executable agent, and the mapping layer between the two. The machine-readable service description (that is a WSDL document) contains network address for the service, the operation it supports and other necessary information for consum-
Fig. 1. The relationships between SOAP, WSDL, and UDDI (Chatterjee and Webber, 2003).
ing the service. The executable agent is responsible for implementing the functionality of the service. The description is separated from the executable agent using a mapping layer. The mapping layer is often implemented using proxies and skeleton in service requester and service provider respectively (Newcomer & Lomow, 2005). This layer is responsible for accepting the message, transforming the XML data to and from the native format of executable agent and finally dispatching the data to the executable agent. On account of separation between executable environment and description of service or separation between semantic and functionality of services in the Web services world, each service can be developed by using any software development platform, operating system, programming language and object model. In this research, Web service standards were adopted and implemented in order to achieve access interoperability between users (client) and the service.
3. Development of a prototype service: a case study A prototype system is developed as part of this research in order to investigate and demonstrate the proposed Web service, which is an on-demand data production system from satellite images. The system is developed based on Web client–server architecture. An automatic road extraction engine, raster-to-vector conversion engine, GML data production engine, WMS, and database construct the major components of the system at the server’s side (Fig. 2). Proper user interfaces at the client’s side are developed for communicating and using the components. A four-tier logical architecture, including a presentation tier, business logic tier, data access tier, and data management tier is adopted for the developing the system (Fig. 2). The presentation tier provides the user with an appropriate and user-friendly interface. This tier is responsible for gathering user inputs, validating user inputs, sending requests with standard statements to the server, and showing results to the user. The business logic tier includes all business rules for the system. As Fig. 2 shows, it includes the above-mentioned engines at the server side that process user requirements (e.g. WMS and feature extraction engine). It is noteworthy that the user interfaces required for interacting with each element of the business logic tier is at the presentation tier. The data access tier interacts with the data management tier to retrieve, store, and remove information. The data access tier does not actually manage or store the data; it merely provides an interface between the business logic and the database. Logically, defining data access as a separate tier enforces separation between the business logic tier and how the application interacts with a database or any other data source. This separation provides the flexibility to choose whether to run the data access code on the same machine as the business logic tier or on a separate machine. It also facilitates the changing of data sources or data access technologies without affecting the application. This is important, because the need to switch from one database vendor to another may arise. The fourth tier (data management) handles the physical retrieval, updating, and deletion of data. This is different from the data access tier, which requests the retrieval, updating, and deletion of data. These operations are often implemented within the context of a full-fledged database management system. As Fig. 2 illustrates, the satellite images and data layers produced are stored and managed in databases at this tier. The present research has developed all of these components. Web service infrastructure is utilized in all interactions between the client and server side applications to achieve access interoperability. WSDL is used to create proxy and skeleton in client side application and business objects of server (e.g. WMS objects)
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Fig. 2. Four-tier logical architecture of the prototype system.
respectively and SOAP is used to transport every interaction (request and response) between the proxy and skeleton. For investigating the system, Kish (an island in Persian Golf), Boushehr (a city in southern Iran), and Rasht (a city in northern Iran) are selected as case study areas. Pan-sharpened Quickbird imagery from Bushehr and pan-sharpened IKONOS images from Kish and Rasht are provided and stored in the database. The following section describes how the service works. In a SDI environment, this service is registered in a registry service and thus searchable in the relevant catalogue service. A catalogue service is a location at which users can search their required data and services based on relevant metadata. If a user cannot find his required data (e.g. required data are not available or are out-of-date), he will search for the on-demand data production service via the catalogue service. After finding and connecting to the service, he can introduce the specifications of the required data (e.g. study area, data layers (road data in this research), and map scale) (Fig. 3). As Fig. 3 shows, the system includes a simple Web mapping interface through which users can select and input the extent of the study area.
The system searches its database for proper satellite images based on the user requirements. After finding suitable image(s), a list of available image(s) is presented to the user (Fig. 4). The user has the capability of viewing available image(s). By clicking on the viewing link of any image, the user allows WMS to run to shown the image (Fig. 5). If there are different applicable images, the user has the option of selecting visually the most appropriate image based on its quality. In addition, WMS provides the user with the capability of introducing the map extent more accurately on the selected image. The next step involves running the automatic feature extraction engine. This engine applies proper feature extraction algorithms to the selected image to extract the required features. To start feature extraction, the number of FCM classes and number of RASF iterations are required from the user. For the case study, these parameters were set to 7 and 5, respectively. In this research, the developed feature extraction algorithm is applied on the selected image. Fig. 6 shows the progress of feature extraction based on the method described earlier.
Fig. 3. An interface through which users can introduce the specifications of the required data.
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Fig. 4. An interface that presents available satellite images fulfilling the user requirements.
Fig. 5. WMS interface for image viewing.
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Fig. 6. Road extraction from a satellite image (a) input Quickbird image of Bushehr Harbor, (b) FCM clustering result, (c) road detected by FCM, (d) image filtered by RASF, and (e) road detected by RASF.
By making a visual comparison between Fig. 6c and e, it can easily be concluded that the road detected with the RASF method is more accurate and reliable than that obtained by FCM. Additionally, the output of RASF can be readily inserted for skeletonization (Fig. 7). Fig. 7a shows that some small, redundant segments in the initially road centerline extracted are connected to the main skeleton. Fig. 7b reveals the pruned skeleton of the road as the final output of the automatic feature extraction method. The extracted features in Fig. 7b are in raster format. Therefore, the raster-to-vector conversion engine is run to convert the extracted road skeleton to a vector in GML format. This engine also applies some constraints on vector data to make them geometrically structured. Then, the vector data is superimposed on the source image and presented to the user (Fig. 8). If the user is satisfied with the result, he can download the extracted features.
By clicking on the download bottom, the GML file is downloaded to the user’s system. Moreover, a copy of the GML data is stored into the database at the data management tier. Metadata is produced for the GML data and then added to the catalogue service to make data searchable for other users in the future. This reduces the number of duplicated efforts producing the same datasets. We observed that the proposed system can resolve the problem of the spatial data gap in a SDI initiative. Since data are offered to users in GML (a standard format in the spatial information community), data interoperability is achieved. Furthermore, as Web service technologies and OGC frameworks are utilized for the development of the system, no problems regarding the interaction between clients and the service exist at the server side; in other words, access interoperability is achieved. Since the system is
Fig. 7. Extracted road: (a) initial road centerline extracted and (b) tuned skeleton as the final output skeleton.
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Fig. 8. An interface that shows extracted feature alone and also superimposed on the source image.
based entirely on OGC and Web service standards, different spatial processing systems that adopt these standards can connect to the service and use it as a complementary processing component. This is the concept behind the distributed geospatial Web services (Peng & Tsou, 2003) sought by the spatial data community. In the prototype service developed, satellite images are stored in a centralized database. The system searches the database to find suitable image(s) for processing. However, developments in a practical system working in a SDI environment will allow required images to be retrieved from various databases owned by different organizations. In other words, different organizations may have satellite images that fulfill the requirements of a single user. In addition to the images available in the service’s database, the system can also try to find and use other data sources from other organizations. When a user connects to the on-demand data production service and introduces his requirements, then, the system will search other databases for suitable imageries. Therefore, this service permits optimal use of available satellite image resources. Better use of the spatial data resources available is one of the main aims of SDI. If available satellite images are not suitable (e.g. because they are out-of-date or of improper resolution), the user can provide satellite images by himself and extract features from his own data sources by further developing the system. Using such an on-demand data production Web service, the user would connect to and use this service to avoid the expense of buying individual software systems for feature extraction. 4. Conclusions and future trends The gap in spatial data is one of the major issues for developing SDIs. This paper proposes a Web-based, on-demand data produc-
tion service on the Internet that uses feature extraction techniques from satellite images as a solution to this problem. In order to investigate this suggestion, a prototype service was developed. OGC frameworks and Web service technologies were utilized for the development of the system; using such standards, data and access interoperability were achieved. The results of investigating the system showed that this service can be helpful in resolving the spatial data gap in different fields like topographical mapping, geological mapping, disaster management, hazard monitoring, and environmental studies. Users can obtain their desired datasets with minimal knowledge about spatial data production. These data can be produced from satellite images that are in the archives of the service, in the databases of other organizations, or provided by the user. Although several automatic feature extraction algorithms exist, these algorithms do not satisfy the automatic production of all data layers in the different applications mentioned above. Therefore, more research regarding automatic feature extraction techniques is still essential. In addition, other solutions must be proposed for extracting features (e.g. those of underground facilities) that are not generally identifiable from satellite images. In order to provide the service with the practical capability for the automatic access to and usage of available satellite images in other databases, proper policy with respect to the ownership of source data, copy right, costs of imageries, and similar topics is essential. These topics should be considered in the context of policy component of SDI initiatives. If such services that take in user requirements and produce data automatically become operational, we would take an evolutionary step toward eBusiness in the spatial data market. SDI initiatives must therefore pay special attention to policies related to eBusiness.
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